-
Notifications
You must be signed in to change notification settings - Fork 45.7k
/
ncf_common.py
352 lines (298 loc) · 12 KB
/
ncf_common.py
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
94
95
96
97
98
99
100
101
102
103
104
105
106
107
108
109
110
111
112
113
114
115
116
117
118
119
120
121
122
123
124
125
126
127
128
129
130
131
132
133
134
135
136
137
138
139
140
141
142
143
144
145
146
147
148
149
150
151
152
153
154
155
156
157
158
159
160
161
162
163
164
165
166
167
168
169
170
171
172
173
174
175
176
177
178
179
180
181
182
183
184
185
186
187
188
189
190
191
192
193
194
195
196
197
198
199
200
201
202
203
204
205
206
207
208
209
210
211
212
213
214
215
216
217
218
219
220
221
222
223
224
225
226
227
228
229
230
231
232
233
234
235
236
237
238
239
240
241
242
243
244
245
246
247
248
249
250
251
252
253
254
255
256
257
258
259
260
261
262
263
264
265
266
267
268
269
270
271
272
273
274
275
276
277
278
279
280
281
282
283
284
285
286
287
288
289
290
291
292
293
294
295
296
297
298
299
300
301
302
303
304
305
306
307
308
309
310
311
312
313
314
315
316
317
318
319
320
321
322
323
324
325
326
327
328
329
330
331
332
333
334
335
336
337
338
339
340
341
342
343
344
345
346
347
348
349
350
351
352
# Copyright 2024 The TensorFlow Authors. All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
"""Common functionalities used by both Keras and Estimator implementations."""
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
import json
import os
from absl import flags
from absl import logging
import numpy as np
import tensorflow as tf, tf_keras
from official.common import distribute_utils
from official.recommendation import constants as rconst
from official.recommendation import data_pipeline
from official.recommendation import data_preprocessing
from official.recommendation import movielens
from official.utils.flags import core as flags_core
FLAGS = flags.FLAGS
def get_inputs(params):
"""Returns some parameters used by the model."""
if FLAGS.download_if_missing and not FLAGS.use_synthetic_data:
movielens.download(FLAGS.dataset, FLAGS.data_dir)
if FLAGS.seed is not None:
np.random.seed(FLAGS.seed)
if FLAGS.use_synthetic_data:
producer = data_pipeline.DummyConstructor()
num_users, num_items = movielens.DATASET_TO_NUM_USERS_AND_ITEMS[
FLAGS.dataset]
num_train_steps = rconst.SYNTHETIC_BATCHES_PER_EPOCH
num_eval_steps = rconst.SYNTHETIC_BATCHES_PER_EPOCH
else:
num_users, num_items, producer = data_preprocessing.instantiate_pipeline(
dataset=FLAGS.dataset,
data_dir=FLAGS.data_dir,
params=params,
constructor_type=FLAGS.constructor_type,
deterministic=FLAGS.seed is not None)
num_train_steps = producer.train_batches_per_epoch
num_eval_steps = producer.eval_batches_per_epoch
return num_users, num_items, num_train_steps, num_eval_steps, producer
def parse_flags(flags_obj):
"""Convenience function to turn flags into params."""
num_gpus = flags_core.get_num_gpus(flags_obj)
batch_size = flags_obj.batch_size
eval_batch_size = flags_obj.eval_batch_size or flags_obj.batch_size
return {
"train_epochs": flags_obj.train_epochs,
"batches_per_step": 1,
"use_seed": flags_obj.seed is not None,
"batch_size": batch_size,
"eval_batch_size": eval_batch_size,
"learning_rate": flags_obj.learning_rate,
"mf_dim": flags_obj.num_factors,
"model_layers": [int(layer) for layer in flags_obj.layers],
"mf_regularization": flags_obj.mf_regularization,
"mlp_reg_layers": [float(reg) for reg in flags_obj.mlp_regularization],
"num_neg": flags_obj.num_neg,
"distribution_strategy": flags_obj.distribution_strategy,
"num_gpus": num_gpus,
"use_tpu": flags_obj.tpu is not None,
"tpu": flags_obj.tpu,
"tpu_zone": flags_obj.tpu_zone,
"tpu_gcp_project": flags_obj.tpu_gcp_project,
"beta1": flags_obj.beta1,
"beta2": flags_obj.beta2,
"epsilon": flags_obj.epsilon,
"match_mlperf": flags_obj.ml_perf,
"epochs_between_evals": flags_obj.epochs_between_evals,
"keras_use_ctl": flags_obj.keras_use_ctl,
"hr_threshold": flags_obj.hr_threshold,
"stream_files": flags_obj.tpu is not None,
"train_dataset_path": flags_obj.train_dataset_path,
"eval_dataset_path": flags_obj.eval_dataset_path,
"input_meta_data_path": flags_obj.input_meta_data_path,
}
def get_v1_distribution_strategy(params):
"""Returns the distribution strategy to use."""
if params["use_tpu"]:
# Some of the networking libraries are quite chatty.
for name in [
"googleapiclient.discovery", "googleapiclient.discovery_cache",
"oauth2client.transport"
]:
logging.getLogger(name).setLevel(logging.ERROR)
tpu_cluster_resolver = tf.distribute.cluster_resolver.TPUClusterResolver(
tpu=params["tpu"],
zone=params["tpu_zone"],
project=params["tpu_gcp_project"],
coordinator_name="coordinator")
logging.info("Issuing reset command to TPU to ensure a clean state.")
tf.Session.reset(tpu_cluster_resolver.get_master())
# Estimator looks at the master it connects to for MonitoredTrainingSession
# by reading the `TF_CONFIG` environment variable, and the coordinator
# is used by StreamingFilesDataset.
tf_config_env = {
"session_master":
tpu_cluster_resolver.get_master(),
"eval_session_master":
tpu_cluster_resolver.get_master(),
"coordinator":
tpu_cluster_resolver.cluster_spec().as_dict()["coordinator"]
}
os.environ["TF_CONFIG"] = json.dumps(tf_config_env)
distribution = tf.distribute.TPUStrategy(
tpu_cluster_resolver, steps_per_run=100)
else:
distribution = distribute_utils.get_distribution_strategy(
num_gpus=params["num_gpus"])
return distribution
def define_ncf_flags():
"""Add flags for running ncf_main."""
# Add common flags
flags_core.define_base(
model_dir=True,
clean=True,
train_epochs=True,
epochs_between_evals=True,
export_dir=False,
run_eagerly=True,
stop_threshold=True,
num_gpu=True,
distribution_strategy=True)
flags_core.define_performance(
synthetic_data=True,
dtype=True,
fp16_implementation=True,
loss_scale=True,
enable_xla=True,
)
flags_core.define_device(tpu=True)
flags_core.define_benchmark()
flags.adopt_module_key_flags(flags_core)
movielens.define_flags()
flags_core.set_defaults(
model_dir="/tmp/ncf/",
data_dir="/tmp/movielens-data/",
dataset=movielens.ML_1M,
train_epochs=2,
batch_size=99000,
tpu=None)
# Add ncf-specific flags
flags.DEFINE_boolean(
name="download_if_missing",
default=True,
help=flags_core.help_wrap(
"Download data to data_dir if it is not already present."))
flags.DEFINE_integer(
name="eval_batch_size",
default=None,
help=flags_core.help_wrap(
"The batch size used for evaluation. This should generally be larger"
"than the training batch size as the lack of back propagation during"
"evaluation can allow for larger batch sizes to fit in memory. If not"
"specified, the training batch size (--batch_size) will be used."))
flags.DEFINE_integer(
name="num_factors",
default=8,
help=flags_core.help_wrap("The Embedding size of MF model."))
# Set the default as a list of strings to be consistent with input arguments
flags.DEFINE_list(
name="layers",
default=["64", "32", "16", "8"],
help=flags_core.help_wrap(
"The sizes of hidden layers for MLP. Example "
"to specify different sizes of MLP layers: --layers=32,16,8,4"))
flags.DEFINE_float(
name="mf_regularization",
default=0.,
help=flags_core.help_wrap(
"The regularization factor for MF embeddings. The factor is used by "
"regularizer which allows to apply penalties on layer parameters or "
"layer activity during optimization."))
flags.DEFINE_list(
name="mlp_regularization",
default=["0.", "0.", "0.", "0."],
help=flags_core.help_wrap(
"The regularization factor for each MLP layer. See mf_regularization "
"help for more info about regularization factor."))
flags.DEFINE_integer(
name="num_neg",
default=4,
help=flags_core.help_wrap(
"The Number of negative instances to pair with a positive instance."))
flags.DEFINE_float(
name="learning_rate",
default=0.001,
help=flags_core.help_wrap("The learning rate."))
flags.DEFINE_float(
name="beta1",
default=0.9,
help=flags_core.help_wrap("beta1 hyperparameter for the Adam optimizer."))
flags.DEFINE_float(
name="beta2",
default=0.999,
help=flags_core.help_wrap("beta2 hyperparameter for the Adam optimizer."))
flags.DEFINE_float(
name="epsilon",
default=1e-8,
help=flags_core.help_wrap("epsilon hyperparameter for the Adam "
"optimizer."))
flags.DEFINE_float(
name="hr_threshold",
default=1.0,
help=flags_core.help_wrap(
"If passed, training will stop when the evaluation metric HR is "
"greater than or equal to hr_threshold. For dataset ml-1m, the "
"desired hr_threshold is 0.68 which is the result from the paper; "
"For dataset ml-20m, the threshold can be set as 0.95 which is "
"achieved by MLPerf implementation."))
flags.DEFINE_enum(
name="constructor_type",
default="bisection",
enum_values=["bisection", "materialized"],
case_sensitive=False,
help=flags_core.help_wrap(
"Strategy to use for generating false negatives. materialized has a"
"precompute that scales badly, but a faster per-epoch construction"
"time and can be faster on very large systems."))
flags.DEFINE_string(
name="train_dataset_path",
default=None,
help=flags_core.help_wrap("Path to training data."))
flags.DEFINE_string(
name="eval_dataset_path",
default=None,
help=flags_core.help_wrap("Path to evaluation data."))
flags.DEFINE_string(
name="input_meta_data_path",
default=None,
help=flags_core.help_wrap("Path to input meta data file."))
flags.DEFINE_bool(
name="ml_perf",
default=False,
help=flags_core.help_wrap(
"If set, changes the behavior of the model slightly to match the "
"MLPerf reference implementations here: \n"
"https://github.com/mlperf/reference/tree/master/recommendation/"
"pytorch\n"
"The two changes are:\n"
"1. When computing the HR and NDCG during evaluation, remove "
"duplicate user-item pairs before the computation. This results in "
"better HRs and NDCGs.\n"
"2. Use a different soring algorithm when sorting the input data, "
"which performs better due to the fact the sorting algorithms are "
"not stable."))
flags.DEFINE_bool(
name="output_ml_perf_compliance_logging",
default=False,
help=flags_core.help_wrap(
"If set, output the MLPerf compliance logging. This is only useful "
"if one is running the model for MLPerf. See "
"https://github.com/mlperf/policies/blob/master/training_rules.adoc"
"#submission-compliance-logs for details. This uses sudo and so may "
"ask for your password, as root access is needed to clear the system "
"caches, which is required for MLPerf compliance."))
flags.DEFINE_integer(
name="seed",
default=None,
help=flags_core.help_wrap(
"This value will be used to seed both NumPy and TensorFlow."))
@flags.validator(
"eval_batch_size",
"eval_batch_size must be at least {}".format(rconst.NUM_EVAL_NEGATIVES +
1))
def eval_size_check(eval_batch_size):
return (eval_batch_size is None or
int(eval_batch_size) > rconst.NUM_EVAL_NEGATIVES)
flags.DEFINE_bool(
name="early_stopping",
default=False,
help=flags_core.help_wrap(
"If True, we stop the training when it reaches hr_threshold"))
flags.DEFINE_bool(
name="keras_use_ctl",
default=False,
help=flags_core.help_wrap(
"If True, we use a custom training loop for keras."))
def convert_to_softmax_logits(logits):
"""Convert the logits returned by the base model to softmax logits.
Args:
logits: used to create softmax.
Returns:
Softmax with the first column of zeros is equivalent to sigmoid.
"""
softmax_logits = tf.concat([logits * 0, logits], axis=1)
return softmax_logits